About the Program
- Development of obstetrical biomarkers
- Application of advanced statistical and computational methods in genomics
- Lecture series focusing on computational biology and data analysis methods
- Support to Maternal-Fetal Medicine fellows in the area of experimental design and statistical analysis
- Application of advanced computational biology and bioinformatics methods to the study of clinical conditions in the field of Maternal Fetal Medicine
- Develop novel data analysis techniques for interpretation of genomics data
- Analysis of longitudinal omics data
- What biomarkers to use for early identifications of pregnancy complications and adverse neonatal outcomes?
- Which methodologies are optimal for development of prediction models from genomics data?
- How to customize risk assessment in obstetrics?
Important Points & Discoveries
- Organized the DREAM Preterm Birth Prediction challenge aiming to find maternal blood transcriptomics markers for pregnancy dating and assessment of preterm birth risk
- Described maternal blood proteomics and transcriptomics changes during normal pregnancy
- Identified maternal blood proteomic changes that predict future onset of preeclampsia and fetal death
- Created a customized standard for fetal weight and demonstrated that it improves detection of fetuses at risk of perinatal death
- Developed and assessed methods for gene set and pathway analysis with omics data
- Developed and pipelines to build molecular classifiers from genomics and single-cell genomics data
- Tarca AL, Romero R, Xu Z, Gomez-Lopez N, Erez O, Hsu CD, Hassan SS, Carey VJ. Targeted expression profiling by RNA-Seq improves detection of cellular dynamics during pregnancy and identifies a role for T cells in term parturition. Sci Rep; Sci Rep. 2019 Jan 29;9(1):848.
- Tarca AL, Romero R, Gudicha DW, Erez O, Hernandez-Andrade E, Yeo L, Bhatti G, Pacora P, Maymon E, Hassan SS. A new customized fetal growth standard for African American women: the PRB/NICHD Detroit study. Am J Obstet Gynecol; 218(2S):S679-S91 e4, 2018.
- Tarca AL, Gong X, Romero R, Yang W, Duan Z, Yang H, Zhang C, Wang P. Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge. Comput Toxicol;5:31-37, 2018.
- Tarca AL, Fitzgerald W, Chaemsaithong P, Xu Z, Hassan SS, Grivel JC, Gomez-Lopez N, Panaitescu B, Pacora P, Maymon E, Erez O, Margolis L, Romero R. The cytokine network in women with an asymptomatic short cervix and the risk of preterm delivery. Am J Reprod Immunol, 2017.
- Chaiworapongsa T, Romero R, Erez O, Tarca AL, Conde-Agudelo A, Chaemsaithong P, Kim CJ, Kim YM, Kim JS, Yoon BH, Hassan SS, Yeo L, Korzeniewski SJ. The prediction of fetal death with a simple maternal blood test at 20-24 weeks: a role for angiogenic index-1 (PlGF/sVEGFR-1 ratio). Am J Obstet Gynecol, 217(6):682.e1-682.e13, 2017.
- Romero R, Erez O, Maymon E, Chaemsaithong P, Xu Z, Pacora P, Chaiworapongsa T, Done B, Hassan SS, Tarca AL. The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study. Am J Obstet Gynecol, 217(1):67.e1-67.e21, 2017.
- Erez O, Romero R, Maymon E, Chaemsaithong P, Done B, Pacora P, Panaitescu B, Chaiworapongsa T, Hassan SS, Tarca AL. The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study. PLoS One;12(7):e0181468, 2017.
- Dayarian A, Romero R, Wang Z, Biehl M, Bilal E, Hormoz S, Meyer P, Norel R, Rhrissorrakrai K, Bhanot G, Luo F, Tarca AL. Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge. Bioinformatics, 15;31(4):462-70, 2014.
- Tarca AL, Lauria M, Unger M, Bilal E, Boue S, Kumar Dey K, Hoeng J, Koeppl H, Martin F, Meyer P, Nandy P, Norel R, Peitsch M, Rice JJ, Romero R, Stolovitzky G, Talikka M, Xiang Y, Zechner C, Collaborators ID. Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge. Bioinformatics;29(22):2892-9, 2013.
- Tarca AL, Draghici S, Bhatti G, Romero R. Down-weighting overlapping genes improves gene set analysis. BMC Bioinformatics;13:136, 2012.
- Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim JS, Kim CJ, Kusanovic JP, Romero R. A novel signaling pathway impact analysis. Bioinformatics;25(1):75-82, 2009.
- Tarca AL, Carey VJ, Chen XW, Romero R, Draghici S. Machine learning and its applications to biology. PLoS Comput Biol;3(6):e116, 2007.